Advanced Lane Finding Project¶
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
First, I'll compute the camera calibration using chessboard images¶
In [1]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline
# 1. camera calibration -> mtx, dist pars
# 2. distorsion correction
# 3. binarization
# 4. perspective transform
# 5. lane pixel detection
# 5.1 lane pixel region selection
# 5.2 polyfit
# 6. fitted lines unwrap
# 7. output visual image and curvature position
##################################################################
# 1. CAMERA CALIBRATION
##################################################################
def camera_calibration():
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('camera_cal/calibration*.jpg')
# Step through the list and search for chessboard corners
limg = lgray = None
for fname in images:
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9,6),None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
# Draw and display the corners
img = cv2.drawChessboardCorners(img, (9,6), corners, ret)
#cv2.imshow('img',img)
#cv2.waitKey(500)
limg = img
lgray = gray
#We calculate the distorsion pars and matrix
return cv2.calibrateCamera(objpoints, imgpoints, lgray.shape[::-1], None, None)
ret, mtx, dist, rvecs, tvecs = camera_calibration()
In [2]:
##################################################################
# 2. DISTORSION CORRECTION
##################################################################
#testimg = cv2.imread("./test_images/test1.jpg") #will read in BGR mode
#testimg = cv2.cvtColor(testimg, cv2.COLOR_BGR2RGB)
calimg1 = mpimg.imread("test_images/test1.jpg")
calimg2 = mpimg.imread("camera_cal/calibration1.jpg")
def img_undistort(img, mtx, dist):
return cv2.undistort( img, mtx, dist, None, mtx)
#test
f, ax = plt.subplots(2, 2, figsize=(24, 20))
f.tight_layout()
ax[0][0].imshow(calimg1)
ax[0][0].set_title('Original Image', fontsize=50)
undist = img_undistort( calimg1, mtx, dist )
mpimg.imsave("output_images/undistorted.png", undist)
ax[0][1].imshow( undist )
ax[0][1].set_title('Undistorted Image', fontsize=50)
ax[1][0].imshow(calimg2)
ax[1][0].set_title('Original Image', fontsize=50)
ax[1][1].imshow( img_undistort( calimg2, mtx, dist ) )
ax[1][1].set_title('Undistorted Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [3]:
##################################################################
# 3. BINARIZATION
##################################################################
# absolute sobel
def abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh=(0,255)):
gray = cv2.cvtColor( img, cv2.COLOR_RGB2GRAY )
k = [1,0] if orient=='x' else [0,1]
sobel = cv2.Sobel(gray, cv2.CV_64F, *k, ksize=sobel_kernel)
abs_sobel = np.absolute(sobel)
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
sbinary = np.zeros_like(scaled_sobel)
sbinary[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1] )] = 1
return sbinary
# magnitude threshold
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
# 1) Convert to grayscale
gray = cv2.cvtColor( img, cv2.COLOR_RGB2GRAY )
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Calculate the magnitude
abs_sobelxy = np.sqrt(sobelx**2 + sobely**2)
# 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
abs_sobel_scaled = np.uint8(255*abs_sobelxy/np.max(abs_sobelxy))
# 5) Create a binary mask where mag thresholds are met
sbinary = np.zeros_like(abs_sobel_scaled)
sbinary[ (abs_sobel_scaled >= mag_thresh[0]) & (abs_sobel_scaled <= mag_thresh[1]) ] = 1
# 6) Return this mask as your binary_output image
return sbinary
#direction of threshold
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
# 1) Convert to grayscale
gray = cv2.cvtColor( img, cv2.COLOR_RGB2GRAY )
# 2) x,y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
dirgrad = np.arctan2( np.absolute(sobely), np.absolute(sobelx) )
# 5) Create a binary mask where direction thresholds are met
binary_out = np.zeros_like(dirgrad)
binary_out[ (dirgrad>=thresh[0]) & (dirgrad<=thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_out
# Edit this function to create your own pipeline.
def s_and_sobelx_th(img, s_thresh=(100, 255), sx_thresh=(20, 100)):
img = np.copy(img)
# Convert to HSV color space and separate the V channel
hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HLS).astype(np.float)
l_channel = hsv[:,:,1]
s_channel = hsv[:,:,2]
# Sobel x
sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0) # Take the derivative in x
abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
# Threshold x gradient
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
# Threshold color channel
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1
# Stack each channel
# Note color_binary[:, :, 0] is all 0s, effectively an all black image. It might
# be beneficial to replace this channel with something else.
color_binary = np.dstack(( np.zeros_like(sxbinary), sxbinary, s_binary))
# Combine the two binary thresholds
combined_binary = np.zeros_like(color_binary)
combined_binary[(s_binary == 1) | (sxbinary == 1)] = 1
return combined_binary
# Apply each of the thresholding functions
def sobel_combined(img):
gradx = abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh=(20, 100))
grady = abs_sobel_thresh(img, orient='y', sobel_kernel=3, thresh=(20, 100))
mag_binary = mag_thresh(img, sobel_kernel=9, mag_thresh=(30, 100))
dir_binary = dir_threshold(img, sobel_kernel=15, thresh=(0.7, 1.3))
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
return combined, gradx, grady, mag_binary, dir_binary
(combined,gradx, grady, mag_binary, dir_binary) = sobel_combined(undist)
def thresholding(img):
return s_and_sobelx_th(img)
# Plot the result
f, axes = plt.subplots(4, 2, figsize=(24, 35))
f.tight_layout()
axes[0][0].imshow(undist)
axes[0][0].set_title('Original Image', fontsize=50)
axes[0][1].imshow(gradx, cmap='gray')
axes[0][1].set_title('Thresholded Gradient X', fontsize=50)
axes[1][0].imshow(grady, cmap='gray')
axes[1][0].set_title('Thresholded Gradient Y', fontsize=50)
axes[1][1].imshow(mag_binary, cmap='gray')
axes[1][1].set_title('Magnitude thresh', fontsize=50)
axes[2][0].imshow(dir_binary, cmap='gray')
axes[2][0].set_title('directional th', fontsize=50)
axes[2][1].imshow(combined, cmap='gray')
axes[2][1].set_title('Combined', fontsize=50)
axes[3][0].imshow(s_and_sobelx_th(undist), cmap='gray')
axes[3][0].set_title('s channel + sobel x', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [4]:
##################################################################
# 4. PERSPECTIVE TRANSFORM
##################################################################
pers = undist.copy()
#mpimg.imsave("undist.png", pers)
h,w= pers.shape[0:2]
#these parameters for straight lines are from 'straight_lines.png' after applying undistortion
a,b,c,d = [[577,463],[706,464],[1037,675],[268,675]]
src = np.float32([a,b,c,d])
e,f,g,h = [[320,0], [960,0], [960,720], [320,720]]
dst = np.float32([e,f,g,h])
# For destination points, I'm arbitrarily choosing some points to be
# a nice fit for displaying our warped result
# again, not exact, but close enough for our purposes
#dst = np.float32([[d[0],0],[c[0],0],c,d])
pts = np.int32(src)
pts = pts.reshape(-1,1,2)
cv2.polylines(pers, [pts], True, (255,0,0), thickness=2)
# Given src and dst points, calculate the perspective transform matrix
M = cv2.getPerspectiveTransform(src, dst)
Minv = cv2.getPerspectiveTransform(dst, src)
# Warp the image using OpenCV warpPerspective()
warped_w_lines = cv2.warpPerspective(pers, M, (undist.shape[1], undist.shape[0]))
warped = cv2.warpPerspective(thresholding(undist), M, (undist.shape[1], undist.shape[0]))
binary_warped = np.zeros_like(warped)
binary_warped[warped>0.5] = 1
binary_warped = binary_warped[:680,:,0]
def warp_binarize(img):
warped = cv2.warpPerspective(thresholding(img), M, (img.shape[1], img.shape[0]))
binary_warped = np.zeros_like(warped)
binary_warped[warped>0.5] = 1
return binary_warped[:,:,0]
f, axes = plt.subplots(2, 2, figsize=(24, 20))
f.tight_layout()
axes[0][0].imshow(pers)
axes[0][0].set_title('Original Image', fontsize=50)
axes[0][1].imshow(warped_w_lines, cmap='gray')
axes[0][1].set_title('Perspective Xform', fontsize=50)
axes[1][0].imshow(warp_binarize(undist) , cmap='gray')
axes[1][1].set_title('Thresholding', fontsize=50)
Out[4]:
In [5]:
##################################################################
# 5. LANE DETECTION
##################################################################
from enum import Enum
import copy
class Line():
def __init__(self):
# was the line detected in the last iteration?
self.detected = False
# x values of the last n fits of the line
self.recent_xfitted = []
#average x values of the fitted line over the last n iterations
self.bestx = None
#polynomial coefficients for the most recent fit
self.current_fit = [np.array([False])]
#radius of curvature of the line in some units
self.radius_of_curvature = 0
#distance in meters of vehicle center from the line
self.line_base_pos = None
#x values for detected line pixels
self.allx = None
#y values for detected line pixels
self.ally = None
# plotted pixel values from the fit
self.ploty = None
self.fitx = None
class LineDetect():
class State(Enum):
UNLOCKED=0
LOCKED=1
ONGOING=2
LOST=3
def __init__(self):
self.debug = True
self.state = self.State.UNLOCKED
self.last_detected_lines = None #last detected line
self.last_lines = None #last retunred line
self.lost_count = 0
self.lost_count_max = 5
# Define conversions in x and y from pixels space to meters
self.ym_per_pix = 30/720 # meters per pixel in y dimension
self.xm_per_pix = 3.7/700 # meters per pixel in x dimension
#does an exponential moving average of the line fits. returns EMAed pars
def EMA(self, prev_lines, cur_lines):
alpha = 0.2
if( cur_lines==None):
return None
if(prev_lines==None):
return cur_lines
else:
ret = copy.copy(cur_lines)
ret.lline.current_fit = cur_lines.lline.current_fit * alpha + prev_lines.lline.current_fit * (1-alpha)
ret.rline.current_fit = cur_lines.rline.current_fit * alpha + prev_lines.rline.current_fit * (1-alpha)
ret.avgcurv = cur_lines.avgcurv*alpha + prev_lines.avgcurv*(1-alpha)
return ret
class FindLineResult():
def __init__(self):
self.detected = False
self.lline = None
self.rline = None
self.leftcurv = 0
self.rightcurv = 0
self.avgcurv = 0
self.distcenter = 0
# Detects lines. Returns None if no lines detected
def find_lines(self, binary_warped):
# lines selection
if self.state == self.State.UNLOCKED:
ret = self.find_lines_cold(binary_warped)
if ret.detected:
self.lost_count = 0
self.state = self.State.ONGOING
self.last_detected_lines = ret
else:
self.lost_count += 1
elif self.state == self.State.ONGOING:
ret = self.find_lines_ongoing(binary_warped, self.last_detected_lines.lline.current_fit, self.last_detected_lines.rline.current_fit)
if ret.detected:
self.lost_count = 0
self.last_detected_lines = ret
else:
self.lost_count += 1
self.state = self.State.UNLOCKED
else:
raise Exception("wrong state")
self.last_lines = self.EMA( self.last_lines, self.last_detected_lines )
return [self.lost_count< self.lost_count_max and self.last_lines, self.last_lines]
# returns true/false to validate the lines
def lines_validation(self, lline, rline):
# calculate the stdev of the width of the lines. should be paralellish
width = rline.fitx - lline.fitx
ret = True
ret = ret and np.std(width) < 50
return ret
# finds lines inside an area given from a previous polynomial fit
def find_lines_ongoing(self, binary_warped, left_fit, right_fit):
lline, rline = Line(), Line()
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] - margin))
& (nonzerox < (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] - margin))
& (nonzerox < (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
lline.allx = leftx
lline.ally = lefty
rline.allx = rightx
rline.ally = righty
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
lline.ploty = ploty
lline.fitx = left_fitx
rline.ploty = ploty
rline.fitx = right_fitx
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
out_img = np.uint8( out_img )
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
#draw lines
cv2.polylines( out_img, [np.int32(np.column_stack((left_fitx, ploty)))] , False, (255,255,0), thickness=4 )
cv2.polylines( out_img, [np.int32(np.column_stack((right_fitx, ploty)))] , False, (255,255,0), thickness=4 )
rline.img = out_img
lline.img = out_img
# bottom center pixel
yb = binary_warped.shape[0]-1
left_centerx = left_fitx[yb]
right_centerx = right_fitx[yb]
centerx = (left_centerx + right_centerx) / 2
center_offset = binary_warped.shape[1]/2 - centerx
lline.current_fit = left_fit
rline.current_fit = right_fit
ret = LineDetect.FindLineResult()
ret.detected = self.lines_validation(lline, rline)
ret.lline = lline
ret.rline = rline
l, r = self.calculate_curvature(left_fit, right_fit)
ret.leftcurv = l
ret.rightcurv = r
ret.avgcurv = (l+r)/2
ret.distcenter = center_offset * self.xm_per_pix
return ret
def find_lines_cold(self, binary_warped, plot=False):
lline, rline = Line(), Line()
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[int(binary_warped.shape[0]/2):,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
out_img = np.uint8( out_img )
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),color=(0,255,0), thickness=2 )
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high)
& (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high)
& (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
lline.allx = leftx
lline.ally = lefty
rline.allx = rightx
rline.ally = righty
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
lline.ploty = ploty
lline.fitx = left_fitx
rline.ploty = ploty
rline.fitx = right_fitx
# bottom center pixel
yb = binary_warped.shape[0]-1
left_centerx = left_fitx[yb]
right_centerx = right_fitx[yb]
centerx = (left_centerx + right_centerx) / 2
center_offset = binary_warped.shape[1]/2 - centerx
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
#draw lines
cv2.polylines( out_img, [np.int32(np.column_stack((left_fitx, ploty)))] , False, (255,255,0), thickness=4 )
cv2.polylines( out_img, [np.int32(np.column_stack((right_fitx, ploty)))] , False, (255,255,0), thickness=4 )
if(plot):
plt.figure()
plt.imshow(out_img)
#plt.plot(left_fitx, ploty, color='yellow')
#plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
lline.detected = True
lline.recent_xfitted = None
lline.bestx = None
lline.best_fit = None
lline.current_fit = left_fit
lline.radius_of_curvature = self.calculate_curvature(left_fit, right_fit)[0]
lline.line_base_pos = 0 #distance in meters of vehicle center from the line
lline.img = out_img
rline.detected = True
rline.recent_xfitted = None
rline.bestx = None
rline.best_fit = None
rline.current_fit = right_fit
rline.radius_of_curvature = self.calculate_curvature(left_fit, right_fit)[1]
rline.line_base_pos = 0 #distance in meters of vehicle center from the line
rline.img = out_img
ret = LineDetect.FindLineResult()
ret.detected = self.lines_validation(lline, rline)
ret.lline = lline
ret.rline = rline
ret.leftcurv = lline.radius_of_curvature
ret.rightcurv = rline.radius_of_curvature
ret.avgcurv = (ret.leftcurv + ret.rightcurv)/2
ret.distcenter = center_offset * self.xm_per_pix
return ret
#calculates curvature in meters from the warped binary polynomial fit
def calculate_curvature(self, left_fit, right_fit):
y_eval = 720
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
sx = xm_per_pix
sy = ym_per_pix
# see writeup for derivation of this formulas
left_fit_cr = [(sx*left_fit[0])/(sy**2), (sx*left_fit[1])/sy, sx*left_fit[2]]
right_fit_cr = [(sx*right_fit[0])/(sy**2), (sx*right_fit[1])/sy, sx*right_fit[2]]
# Calculate the new radii of curvature
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
# Now our radius of curvature is in meters
return(left_curverad, right_curverad)
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##################################################################
# 6. UNWRAP
##################################################################
def unwrap(undist, warped_shape, left_fit, right_fit):
# Generate x and y values for plotting
ploty = np.linspace(0, warped_shape[0]-1, warped_shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Create an image to draw the lines on
warp_zero = np.zeros(warped_shape).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (color_warp.shape[1], color_warp.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(np.uint8(undist), 1, newwarp, 0.3, 0)
return result
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In [7]:
##################################################################
# 7. PIPELINE
##################################################################
class Pipeline():
def __init__(self):
self.lline = None
self.rline = None
self.ldetect = LineDetect()
def run(self, img, unwrapOut=False):
#undistort
undist = img_undistort( img, mtx, dist )
#wark and bin
binary_warped = warp_binarize(undist)
#line detect
valid, result = self.ldetect.find_lines(binary_warped)
if(valid):
out_img = unwrap(undist[:,:,:], binary_warped.shape[0:2], result.lline.current_fit, result.rline.current_fit) if unwrapOut else result.lline.img
#add text stats
cv2.putText(out_img,"curvature: {0:d} m".format(int((result.avgcurv)/2.0)), (20,100), cv2.FONT_HERSHEY_SIMPLEX, 2, 255, thickness=4)
cv2.putText(out_img,"center off: {0:.2f} m".format(result.distcenter), (20,200), cv2.FONT_HERSHEY_SIMPLEX, 2, 255, thickness=4)
else:
out_img = undist
return out_img
# Make a list of test images
timages = glob.glob('test_images/*.jpg')
for im in timages:
print(im)
p = Pipeline() # we instanstiate a pipeline every single time to test individual images
out = p.run(mpimg.imread(im), True)
plt.figure()
plt.imshow(out)
mpimg.imsave(im+"w", out)
In [8]:
##################################################################
# 8. MOVIE CREATIONG
##################################################################
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
def process_image(image):
# NOTE: The output you return should be a color image (3 channel) for processing video below
# TODO: put your pipeline here,
# you should return the final output (image with lines are drawn on lanes)
return p.run(image, unwrapOut=True)
p = Pipeline()
white_output = 'output.mp4'
clip1 = VideoFileClip("project_video.mp4", audio=False).subclip(0)
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
%time white_clip.write_videofile(white_output, audio=False)
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